Developer Name: Code Everest Pvt | Algorithm Name: facex_002 | Algorithm Type: 1:1 Verification

Date of Algorithm Submission: 2021_08_24 | Date of Report Card Generation: 2022-02-24

The report shows accuracy improvements over time for this developer. The traces correspond to the datasets named in the legend. The FMR is fixed independently for each dataset to the value given in the y-axis label.

For each dataset, the panels show false non-match rates vs. false match rates for facex_002 and several of the most accurate algorithms listed in the caption. The most accurate algorithms vary by dataset. When negative values appear on the vertical axis, they are logarithms of FNMR. Use mouseover to see FNMR, FMR and threshold values.

Dataset comparison: false non-match rates vs. false match rates for each dataset. Use mouseover to see FNMR, FMR and threshold values.

To inform threshold setting, the two panels show, respectively, FMR and FNMR as a function of threshold. Many applications will use the threshold to target a specific FMR, established by policy. For a given threshold, FNMR variation is expected across datasets due, primarily, to quality and agageing differences. FMR differences may be due to different demographic composition - see figure below - or other factors.

For women, left, and men, the panels show false non-match rates when mediocre border cross photos are compared against high quality reference application portraits collected from individuals born in the country identified on the horizontal axis and aged either above or below 45 years of age at the time of the application photo. The square dots give the empirical FNMR point estimate. The vertical lines give bootstrap 95-percent confidence intervals around the point estimate. The intervals are wider when the country and age group is less-represented in this dataset. Overlapping intervals is an indication of no significant difference. Low FNMR values are synonymous with high accuracy.

Operating on visa images, the heatmap shows false match observed over impostor comparisons of faces from different individuals who have the given age pair. False matches are counted against a recognition threshold fixed globally to give FMR = 0.0001 over all on the order of 10^10 impostor comparisons. The text in each box gives the same quantity as that coded by the color. Light colors present a security vulnerability to, for example, a passport gate.

Operating on visa images, the heatmap shows false match rates observed over impostor comparisons of faces from different individuals who were born in the given region pair. False matches are counted against a recognition threshold fixed globally to give the target FMR in the plot title, computed over all on the order of 10^10 impostor comparisons. If text appears in each box it gives the same quantity as that coded by the color. Grey indicates FMR is at the intended FMR target level. Light red colors present a security vulnerability to, for example, a passport gate. Each +1 increase in log10 FMR corresponds to a factor of 10 increase in FMR. The matrix is not quite symmetric because images in the enrollment and verification sets are different.

Operating on visa images, the heatmap shows false match rates observed over impostor comparisons of faces from different individuals who were born in the given country pair. False matches are counted against a recognition threshold fixed globally to give the target FMR in the plot title, computed over all on the order of 10^10 impostor comparisons. If text appears in each box it gives the same quantity as that coded by the color. Grey indicates FMR is at the intended FMR target level. Light red colors present a security vulnerability to, for example, a passport gate. Each +1 increase in log10 FMR corresponds to a factor of 10 increase in FMR. The matrix is not quite symmetric because images in the enrollment and verification sets are different.